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The author's boastful tweet should really be examined. Can a deep-learning stack write better code than a person? Although, really, the more important question

The author's boastful tweet should really be examined. Can a deep-learning stack write better code than a person? Although, really, the more important question: Can we do studies on this without comparing expert computers to undergraduates who are just learning?

choeger 6y ago
Sure it can. For algorithms that fit a particular form, you could always automatically derive an optimal solution (think of dynamic programming). The actual question is: When is a neural net a good idea? Personally, I think it will always be great when the output goes to a human. So speech synthesis is a really great example. The same holds for game AIs. But things that go into a "software 1.0" system (anything that needs to be understood by humans)? It will be difficult. Imagine recognition for self-driving cars sounds great until you have the first deadly accident that you simply cannot explain. Your system might even be really, really good, but that won't reduce the liability in that one case where it doesn't work.
- Kednicma 6y ago
  Interesting ideas. How long should it take to create one of these solutions? NP-completeness doesn't care whether the thinker is human or neural-net, and optimality can take exponentially long to find. Also, what if there simply isn't an optimal solution? Matrix multiplication comes to mind; we think it's quadratic-time, given the asymptotic number of operations that ought to be needed, but all existing algorithms are cubic-time (rounded up), and possibly there isn't a quadratic-time algorithm.
- Jasper_ 6y ago
  Game AIs are terrible for neural networks. Game designers want the ability to micro-tune difficulty and decision making to pace the game experience and the curve, and often times what's engaging for players is a simple model they can reason about and understand. I think there's probably some value in neural networks in game design, but I don't see it being prevalent at runtime, mostly just in the tooling.

choeger 6y ago

Sure it can. For algorithms that fit a particular form, you could always automatically derive an optimal solution (think of dynamic programming). The actual question is: When is a neural net a good idea? Personally, I think it will always be great when the output goes to a human. So speech synthesis is a really great example. The same holds for game AIs. But things that go into a "software 1.0" system (anything that needs to be understood by humans)? It will be difficult. Imagine recognition for self-driving cars sounds great until you have the first deadly accident that you simply cannot explain. Your system might even be really, really good, but that won't reduce the liability in that one case where it doesn't work.

Kednicma 6y ago
Interesting ideas. How long should it take to create one of these solutions? NP-completeness doesn't care whether the thinker is human or neural-net, and optimality can take exponentially long to find. Also, what if there simply isn't an optimal solution? Matrix multiplication comes to mind; we think it's quadratic-time, given the asymptotic number of operations that ought to be needed, but all existing algorithms are cubic-time (rounded up), and possibly there isn't a quadratic-time algorithm.
Jasper_ 6y ago
Game AIs are terrible for neural networks. Game designers want the ability to micro-tune difficulty and decision making to pace the game experience and the curve, and often times what's engaging for players is a simple model they can reason about and understand. I think there's probably some value in neural networks in game design, but I don't see it being prevalent at runtime, mostly just in the tooling.